Mobile robots have been deployed in multiple types of environments including one or more people, such as offices and hospitals. Such robots can provide assistance in home, office and medical environments, but need to verbally and physically interact with people in the environment. For example, a robot can verbally provide information or physically retrieve an object to aid one or more people in the environment.
However, to effectively interact with people and perform tasks, it is desirable for a robot to model the location of people and other task-related entities in its surrounding environment. Tracking entities, such as people, allows the robot to plan efficient sequences of actions for accomplishing given tasks. Conventional methods for entity tracking have concentrated on tracking movement of people in the immediate vicinity of the robot over short time periods using lasers and radio frequency identification (“RFID”) sensors.
These conventional methods, however, focus on short-term entity tracking and are unable to track an entity after the entity leaves the robot's field of view. Further, the use of lasers prevents these conventional methods from differentiating people from other objects or obstacles in the environment. Additionally, large-scale use of RFID sensors is impractical. Hence, conventional methods merely allow robots to track entities for short time intervals.
Thus, what is needed is a system and method for tracking entities over extended time intervals.